Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

Abstract

There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

“…Computational tools have been applied to identify drug-like antibodies that have favorable stability. 4 For viscosity prediction, Sharma et al found that viscosity is highly correlated with variable fragment (Fv) net charge and charge symmetry and weakly correlated with hydrophobicity. 5 Based on these three parameters, a linear equation was proposed to calculate viscosity at 180 mg/ml (pH 5.5 and 200 mM arginine-HCl).…”

Physiologically Based Modeling to Predict Monoclonal Antibody Pharmacokinetics in Humans from in vitro Physiochemical Properties

“…Since self-association interactions are mechanistically more directly related to the solubility, viscosity and opalescence of the antibody formulation than to nonspecific antibody interactions that occur in vivo , it may be more difficult to identify any potential associations between AC-SINS and PBPK model-based parameters characterizing nonspecific interactions. 20 Further PBPK studies using additional antibodies would be needed to explore AC-SINS as a model-based predictor of antibody PK in humans.…”

Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment

“…Several experimental assays exist to assess antibody developability. 10 To measure polyspecificity, i.e., the propensity of an antibody to bind off-target molecules, binding to baculovirus particles, polyspecificity reagent, or cross-interaction chromatography have been explored. 11–13 In a recent study, polyspecificity was associated with poor clearance in human clinical trials, making it a critical parameter to consider in antibody lead selection.…”

“…Assays that are more demanding of protein quantity are also critical in assessing developability. 10 For example, antibodies delivered through subcutaneous injections require formulation and administration at high concentrations. 15 Highly viscous antibodies provide substantial challenges to subcutaneous delivery and manufacturing, making it necessary to identify antibodies with low viscosity to reduce the risk of clinical failure and increase patient compliance.…”